In this discussion, The Hated One, a tutorial creator and expert in local AI, shares crucial insights on maintaining privacy while using chatbots. He emphasizes the advantages of running AI locally, revealing how open-source models let you fine-tune performance while keeping your data safe. Listeners learn about essential tools like Ollama and the steps to set up local AI environments using Docker. This conversation not only demystifies AI terminology but also empowers you to take control of your data in an increasingly digital world.
Running AI models locally allows users to retain control over their data, mitigating privacy concerns associated with third-party servers.
Understanding the technical aspects of LLMs, including parameters and models like LLAMA, is essential for effective local AI usage.
Deep dives
The Privacy Risks of AI Chatbots
Using popular AI chatbots raises significant privacy concerns as user data is frequently sent to third-party servers. This data collection allows companies to analyze and store personal information, leading to a loss of control over where the data goes. In an age where privacy is paramount, users can no longer assume that their interactions with AI remain confidential. Alternatives exist, such as running AI models locally, which keeps all data on the user's device and mitigates privacy concerns.
Understanding Local AI and LLMs
Local Large Language Models (LLMs) can be run on personal devices, ensuring that no data is transmitted externally. An LLM is trained on vast amounts of text data and relies on parameters to recognize complex patterns within language, allowing for sophisticated responses. The distinction between models, AI engines, and user interfaces is critical, as multiple layers are involved in interacting with LLMs, including structures like LLAMA and Ollama for user-friendly access. By running an LLM locally, users can maintain control over their data while still experiencing powerful AI capabilities.
Setting Up Local AI Models
To run AI models locally, users must first download models and use compatible interfaces like Olama to interact with them. The installation process is streamlined and allows for easy access to a variety of models while maintaining privacy. Users should consider the model's size, as some, like the 400 billion parameter version of the LLAMA model, may be too demanding for typical consumer hardware. Utilizing platforms like OpenWebUI can enhance the experience, making it easier to customize and manage the models locally without compromising data security.
AI chatbots are taking over the world. But if you want to guarantee your privacy when using them, running an LLM locally is going to be your best bet. In this video I collaborate with The Hated One to show you how, and to explain some AI terminology so that you understand what's going on.
00:00 Your Data is Used to Train Chatbots 01:11 Understanding AI Models 01:28 LLM 02:23 Parameters 03:34 Size 04:51 AI Engine and UX 07:15 Tutorial, courtesy of The Hated One 08:07 Ollama 12:40 Open Web UI installation 13:23 Docker 15:30 Setting up Open Web UI 16:38 Choose to Take Control of Your Data
The biggest advantage of open source models is that you can fine tune them using your own instructions while keeping all data private and confidential. Why trust your data to someone else when you don’t have to?